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Design the AutoGen Agent System Architecture
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Linked challenge: Multi-Agent System for Internal Security Anomaly Detection
Format
Code-aware
Lines
7
Sections
1
Linked challenge
Multi-Agent System for Internal Security Anomaly Detection
Prompt source
Original prompt text with formatting preserved for inspection.
7 lines
1 sections
No variables
1 code block
Using AutoGen, design a multi-agent conversational system to detect security anomalies. Define at least three distinct agent roles (e.g., Log Monitor, News Analyst, Security Investigator, Reporting Agent), their respective capabilities, and how they will communicate and collaborate to achieve the task. Specify the communication patterns (e.g., hierarchical, peer-to-peer) and the data flow between them. Your design should consider how o4-mini will be integrated into the core reasoning of key agents. Provide an AutoGen configuration sketch. ```python
import autogen config_list = autogen.config_list_from_json( 'OAI_CONFIG_LIST', filter_dict={ 'model': ['o4-mini'], },
) # Define agent roles and their initial configurations here
# e.g., researcher = autogen.AssistantAgent(name="Researcher", llm_config={"config_list": config_list})
# code_executor = autogen.UserProxyAgent(name="CodeExecutor", code_execution_config={"last_n_messages": 3, "work_dir": "coding"}) # Define the agent collaboration flow
# e.g., autogen.Completion.start_chat(researcher, llm_config={"config_list": config_list}, message="Initial query...")
```Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Tune next
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Verify after
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.